Compared with the above adversarial-based method, difference-based methods, such as DAN [1]}, BNM [2]}, SimNet [3]}, SWD [4]}, MSTN+DSBN [5]}, TPN [6]}, CGDM [7]}, SHOT [8]} and TransDA [9]}, introduces specific metrics or function layers to quantify the difference of prototypes and align the features between source and target domains by minimizing the difference of prototypes between domains. As shown in Table REF , most difference-based networks achieve better results than adversarial-based since they focus more on the class-discriminative information, especially TransDA [9]} with an accuracy rate of 83.0\(\%\) . In contrast, the proposed SAFF utilizes information from all domains to synthesize object-invariant features to further enhance attention, so TVT+SAFF can align the object region better, and improve inter-domain generalization and robustness.
In addition, DMRL [11]} and DM-ADA [12]} mix inter-domain pixel-level or feature-level representations to obtain a more continuous domain-invariant latent space and help match the global domain statistics across different domains, with an accuracy of 75.5\(\%\)  and 75.6\(\%\) , respectively. However, the weighted-based element mixing strategy of elements may lead to the blurring and out-of-bounds of the objective semantics, resulting in the decline of discriminability. Instead, SAFF overcomes this problem by using labeled synthetic information. Moreover, BSP+TSA [13]}, with 82.0\(\%\) , enriches internal feature patterns in the latent space by generating cross-domain features through random sampling enhancement. However, unlike the proposed SAFF, the enhancement approach adopted by BSP+TSA is unidirectional and does not consider the enhancement of information between different classes. In summary, as shown in Table REF , TVT+SAFF achieves the best performance with an accuracy of 85.40\(\%\) , 2.89\(\%\)  higher than the second-placed TransDA [9]}. In particular, as shown in Table REF , SAFF can consistently improve the performance of three mainstream vision transformer-based backbones. Specifically, SAFF empowers SWIN, BOAT, and TVT with 10.22\(\%\) , 6.90\(\%\) , and 0.99\(\%\)  improvement, respectively. Based on these promising results, we can infer that SAFF can explore truly useful information to stably enhance the feature transferability and discriminability of the classifier on this difficult cross-domain dataset.
